Tracking Discontinuous Motion Using Bayesian Inference

Abstract

Robustly tracking people in visual scenes is an important task for surveillance, human-computer interfaces and visually mediated interaction. Existing attempts at tracking a person’s head and hands deal with ambiguity, uncertainty and noise by intrinsically assuming a consistently continuous visual stream and/or exploiting depth information. We present a method for tracking the head and hands of a human subject from a single view with no constraints on the continuity of motion. Hence the tracker is appropriate for real-time applications in which the availability of visual data is constrained, and motion is discontinuous. Rather than relying on spatio-temporal continuity and complex 3D models of the human body, a Bayesian Belief Network deduces the body part positions by fusing colour, motion and coarse intensity measurements with contextual semantics.